This paper studies a new randomized quasi-Monte Carlo method for estimating the mean and variance of the Pareto distribution. In many Monte Carlo simulations, there are some stability problems for estimating the population Pareto variance by using the sample variance. In this paper, we propose a randomized quasi-random number generator [quasiRNG] to generate Pareto random samples, such that the...

In many practical situations, we need to estimate different statistical characteristics based on a sample. In some cases, we know that the corresponding probability distribution belongs to a known finite-parametric family of distributions. In such cases, a reasonable idea is to use the Maximum Likelihood method to estimate the corresponding parameters, and then to compute the value of the desir...

Recently, Guidorzi et al. (2005) expanded the size of the sample of GRBs for which variabilities and peak luminosities have been measured, from 11 to 32. They confirm the existence of a correlation, but find a dramatically different relationship between L and V than had originally been found. We find that this is the result of improper statistical methodology. When we fit a model to the data th...

COMPARING THE STRUCTURAL COMPONENTS VARIANCE ESTIMATOR AND USTATISTICS VARIANCE ESTIMATOR WHEN ASSESSING THE DIFERENCE BETWEEN CORRELATED AUCs WITH FINITE SAMPLES By Anna Leigh Bosse, M.S. A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University. Virginia Commonwealth University, 2017 Major Director: Dr. Le Kang, Assoc...

We show that the in-sample estimate of the variance of a global minimum risk portfolio constructed using an estimated covariance matrix of returns will on average be strictly smaller than its true variance. Scaling the in-sample estimate upward by a standard degrees-of-freedom related factor or using the Bayes covariance matrix estimator can be inadequate; the correction is likely to be twice a...

Variance estimation is central to many questions in finance and economics. Until now ex-post variance estimation has been based on infill asymptotic assumptions that exploit high-frequency data. This paper offers a new exact finite sample approach to estimating ex-post variance using Bayesian nonparametric methods. In contrast to the classical counterpart, the proposed method exploits pooling o...